Large data sets exist in various sizes and organizational structures. With companies adopting big data formats, data sets are as large as ever. The volume of data collected incident to increased popularity of online and electronic transactions continues to grow as a corollary. For example, billions of records (also referred to as rows) and hundreds of thousands of columns worth of data may populate a single table. The large volume of data may be collected in a raw, unstructured, and undescriptive format in some instances.
Data may be ingested into big data storage formats to convert structured files in formats such as XML or JSON into a format useable by analysts. The distributed processing systems of big data systems may be limited by incoming file formats and ingestion systems restrictive of processing parallelism. Distributed processing systems typically split input files using a record delimiter. Conversion of binary files into delimited files followed by splitting and processing may result in files being processed multiple times. Additional processing typically means additional processing time. As a result, the ingestion systems may not scale up efficiently in big data environments that ingest binary data files.
Data ingestion can also be costly in terms of time. Ingestion projects typically have different parameters that result in custom code. Ingestion projects are frequently delayed as development time is extended by writing the custom code for each incoming data format. Execution times are often long as a result of extra read/write operations as well as locking issues. Moreover, data consistency is often degraded as a result of multi-step processes and dependencies between applications and end users.